Underwater Acoustic Applications Research Articles

Underwater Scattering Exceptional Point by Metasurface with Fluid‐Solid Interaction

AbstractExceptional point (EP), a special degeneracy in non‐Hermitian systems, has exhibited various distinctive wave characteristics. However, the conventional synthesis of scattering EPs in acoustics is confined to rather limited methods for breaking Hermiticity, typically requiring intrinsic losses or open interfaces. Here, the concept of synthesizing scattering EPs is theoretically and experimentally demonstrated by leveraging a metasurface with fluid‐solid interaction (FSI) in water. The incorporation of FSI offers a novel mechanism to customize natural radiation losses. Simulations and experiments consistently confirm that synthesized EPs result in extremely asymmetric scattering patterns, even in the case of spatially impenetrable metasurfaces. This enhanced spatial symmetry breaking benefits from the FSI on the interface and the nonlocal interaction between unit cells. The proposed non‐Hermitian framework, involving the interplay of sound in fluids and solids, is expected to open up new possibilities for exploring unique non‐Hermitian physics and underwater acoustic applications.

Analysis of Transient Thermoacoustic Characteristics and Performance in Carbon Nanotube Sponge Underwater Transducers.

Recent advancements in marine technology have highlighted the urgent need for enhanced underwater acoustic applications, from sonar detection to communication and noise cancellation, driving the pursuit of innovative transducer technologies. In this paper, a new underwater thermoacoustic (TA) transducer made from carbon nanotube (CNT) sponge is designed to achieve wide bandwidth, high energy conversion efficiency, simple structure, good transient response, and stable sound response, utilizing the TA effect through electro-thermal modulation. The transducer has potential application in underwater acoustic communication. An electro-thermal-acoustic coupled simulation for the open model, sandwich model, and encapsulated model is presented to analyze the transient behaviors of CNT sponge TA transducers in liquid environments. The effects of key design parameters on the acoustic performances of both systems are revealed. The results demonstrate that a short pulse excitation with a low duty cycle could greatly improve the heat dissipation of the encapsulated transducer, especially when the thermoacoustic response time becomes comparable to thermal relaxation time.

Trans-dimensional joint inversion of modal amplitude and arrival- time data for geoacoustic properties in layered muddy sediments

Long-range sound fields in a shallow-water waveguide can be expressed as a summation of several dispersive propagating modes. Modal arrival times as a function of frequency, extracted via warping time-frequency analysis, have been used by many researchers to estimate sound speeds and densities for layered models of the seabed sediments. This paper explores joint inversion of modal amplitude spectra together with arrival times to also estimate sediment-layer attenuations. To consider the common case in underwater-acoustic applications where the source amplitude spectrum is unknown, we include this spectrum as unknown parameters in the inversion, formulated implicitly within the likelihood function. Trans-dimensional Bayesian inversion is applied to estimate marginal probability profiles of the geoacoustic properties, providing quantitative uncertainties. The data considered here were collected on the New England Mud Patch during the 2017 Seabed Characterization Experiment. [Work supported by the Office of Naval Research.]

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

A novel broadband underwater sound absorption metastructure with multi-oscillators

Underwater absorptive metamaterials are rapidly developed duo local resonance mechanisms, but they still suffer from the disadvantages of poor hydrostatic resistance. The poor acoustic absorption performance at high hydrostatic pressure mainly attributes to the decreased damping coefficient and increased modulus of substrate at high hydrostatic pressure. Acoustic metastructures with helical oscillators are proposed and optimized with simulating annealing algorithm, where the helical oscillators are beneficial for easing the stress in the polymer substrate and resulting in good acoustic absorption abilities. Four acoustic absorptive metastructures without any oscillators (pure polymer substrate), with cylindrical oscillators, with helical oscillators, with both cylindrical and helical oscillators are proposed and experimentally verified in this study. All three metastructures with oscillators demonstrate excellent acoustic absorption coefficient with the absence of the hydrostatic pressure, while the metastructures with helical oscillators exhibit better hydrostatic pressure resistance compared with the metastructures with cylindrical oscillators. Besides, the coupling of helical oscillators and helical oscillators enhanced the acoustic performance furtherly. It is revealed that the acoustic metamaterials with a combination of cylindrical oscillator and helical oscillator exhibit superior performance under hydrostatic pressure up to 3 MPa in the frequency range of 0.5kHz∼10 kHz compared with the others, demonstrating a new avenue for underwater acoustic metamaterials application.

Thermoacoustic performance of 100 kHz wide-band packaged projector in water tank with three-dimensional graphene

This paper presents the demonstration of underwater thermoacoustic (TA) sound generation using three-dimensional graphene (3D-C). 3D-C is able to interface with water as the immediate acoustic medium, resulting in reduced attenuation across solid packaging encapsulation due to favorable acoustic impedance matching. This is shown using array configurations of 2*2 and 4*4 individual 3D-C elements prepared onto printed circuit boards and secured within a watertight cuboid Delrin container with deionized water. Comparisons of underwater acoustic performance between internal medium of water and air shows significant improvement in measured sound pressure level (SPL) with internal water medium, in contrast with internal air medium where SPL were below noise floor. This demonstration opens up new possibilities of 3D-C for underwater acoustic applications. Simulations are conducted using 3D finite element model generated with COMSOL Multiphysics and validated with experimental results. A revised formulation of transmission voltage response (TVR) specific to TA sound generation is proposed due to the difference in power scaling of TA sound generation and subsequently validated using simulation.

The electromechanical coupling property and vibration behavior in 1-3 type piezoelectric composites

Electromechanical coupling coefficient (kt) of piezoelectric composite is the core indicator for medical ultrasonic, nondestructive testing and underwater acoustic transducers applications. Generally, the kt of piezoelectric composite is susceptible to be interfered by its structure parameter and spurious modes. In this work, we figured out the effect of the polymer on the vibration mode by fixing the size of the piezoelectric rod and changing the content of different softness polymer. The spurious modes were related with resonant standing wave caused by classical Bragg scattering in periodic structures. The propagation of spurious mode vibration can be cut off by soft polymer. The maximum kt of 69% was obtained at a Pb(Zr,Ti)O3 (PZT4) ceramic volume fraction of 24.5%. The kt was 5% ∼ 10% higher than that of other reported PZT4 ceramic composites and comparable to PZT5 ceramic composites. This work presented a paradigm for vibration modes modulation by changing the polymers ratio, which providing ideas for excellent performance acoustic transducer designing.

Low-frequency directional wideband transducer based on lead-free piezoelectric ceramics

In recent years, lead-free piezoelectric ceramic devices have been extensively researched and applied in the high-frequency range, specifically above 20 kHz, for airborne acoustics applications. However, few applications have been reported on low-frequency underwater acoustic transducers, which are crucial in the fields of underwater acoustic communication, ocean resource development, etc., because this imposes more stringent requirements for lead-free piezoelectric ceramics in terms of size, mechanical coupling coefficient, piezoelectric coefficients, and temperature stability. This paper explores the application of high-performance potassium sodium niobate based lead-free piezoelectric ceramics in a low-frequency directional wideband transducer. It covers design, fabrication, and performance testing of the transducer. The transducer measures Φ236 × 83 mm2 and weighs 4.45 kg, which achieves wideband emission (3500–8200 Hz) and directional sound emission with a front-to-back sound pressure ratio exceeding 6 dB across a significant frequency range. Both the theoretical analysis and the experimental results confirm the competitiveness application of the lead-free piezoelectric ceramics in the field of low-frequency underwater acoustic transducers applications.

Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application

In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency fc=9 kHz, and the BER with 10−3 order of magnitude when carrier frequency fc=15.5 kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean.

Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

Simulation-aided machine learning: Applications in underwater acoustic perception and explainability

Underwater acoustic applications of machine learning are challenging due to sparsity, lack of diversity, and often uncertain labeling of training data. This is due to low levels of consumer relevance. One approach to the problem is to collect and label more data. However, this is expensive and unlikely to fully address the issue. A complementary approach is to use simulation for generating synthetic data and augmenting the machine learning pipeline. This also enables explainability by encoding human knowledge of the underlying physics. We present two case studies in simulation-aided machine learning. The first is an application to underwater munition dumpsite characterization using multi-view sonar surveys. Self-supervised learning has been used to pre-train a generative deep neural network to predict alternate views using unlabeled data. Subsequently, training is completed using a small volume of labelled data. Simulation was necessary for successful pre-training to continuously fill viewing angle gaps caused by limited and rigid survey directions. The second application is autonomous tactical planning for uncrewed underwater vehicles. Reinforcement learning policies have been trained to passively detect and localize underwater acoustic sources. These have been trained and evaluated in a fully synthetic environment, using a sophisticated propagation model together with empirical environmental parameters.

Efficient asymmetrical transmission through a metagrating for underwater acoustic waves

Acoustic asymmetrical transmission is a theoretical and engineering challenge because of the reciprocity of the linear acoustic wave equation. It can be achieved by systems breaking reciprocity or by reciprocal systems relying solely on spatial symmetry breaking. Metagratings are planar structures relying on Bragg's diffraction to reroute wave energy toward a desired direction and are eventually able to achieve asymmetrical transmission when build from an asymmetrical pattern of multiple basic elements. The challenge for water-like media is to combine the geometrical complexity of the structure with good acoustic impedance contrast and practical feasibility. In this work, we build a reciprocal metagrating from brass cylinders arranged according to a numerically optimized pattern and obtain highly efficient asymmetrical transmission for underwater acoustic waves. Around 200 kHz, the structure transmits nearly all incident energy toward a 45° angle when insonified from one side, but act as a near perfect reflector when insonified from the other. The effect relies entirely on the simple phenomena of linear wave diffraction and interference. The generality and efficiency of this device could be of interest for applications in underwater acoustics or medical ultrasounds.

Non-iterative constrained compressive beamforming

Compressive beamforming is increasingly researched and employed in underwater acoustic applications. Sparse Bayesian learning (SBL) induces the prior distribution and encourages the sparsity of the source vector, achieving improved accuracy compared with other deterministic compressive beamforming approaches, where the constraint optimization is solved explicitly. Considering the structural complexity of targets and underwater environment, it benefits the reconstruction performance to employ the multiconstraint SBL (MSBL) approach, exploring the sparsity in a joint sparse representation domain. However, the computation load of MSBL is heavy and unpredictable due to the requisite iterative implementation, and regular acceleration approaches are difficult to employ. To overcome such obstacles, an online constrained SBL is proposed, which is non-iterative and computationally efficient. Moreover, the estimation convergence of hyperparameters is proved and guaranteed theoretically. Through empirical and experimental results, the efficacy of the proposed method is validated and compared with other state-of-the-art beamforming approaches.

Uncertainty quantification of deep learning based direction-of-arrival estimation with conformal prediction

Direction-of-arrival (DOA) estimation is an important task in array signal processing with applications in underwater acoustics, biomedicine, geophysics, and robotics. The signals recorded on a spatially distributed array along with an analytical model of wave propagation, are used to estimate the unknown DOA. Recently, end-to-end deep learning-based methods have been proposed for improving DOA estimation performance. Yet, real-time DOA estimation has a number of challenges such as sensor noise, reverberant surroundings, uncertainty in sensor locations and environment. In this work, we propose to use conformal prediction for uncertainty quantification in DOA estimation. Conformal prediction is a statistically rigorous method to provide confidence intervals for an estimated quantity without making distributional assumptions. With conformal prediction, confidence intervals are computed via quantiles of user-defined score values. This easy-to-use method can be applied to any trained classification/regression model as long as an appropriate score function is chosen. The proposed approach shows potential to enhance the real-time applicability of deep learning methods for DOA estimation. We illustrate the advantages of conformal prediction for different deep-learning methods for DOA estimation. To the best of our knowledge, this is the first analysis of using conformal prediction for uncertainty quantification of deep learning methods for acoustic estimation.

Ray tracing of long-range underwater acoustic vortex wave propagation

The underwater acoustic communications environment is severely band-limited, which leads to a bottleneck in data transfer. Existing methods of data transfer in underwater acoustic communications applications typically rely primarily on conventional temporal and frequency modulation techniques and achieve bit rates peaking at approximately 40 kb/s. One method of easing the bottleneck and increasing the data rate is to explore further potential degrees of freedom which may be utilized. Acoustic orbital angular momentum (OAM) is a physical quantity that characterizes the rotation in a propagating helical pressure wavefront. The unique phase patterns of OAM carrying vortex waves form an orthogonal basis which may be useful as an additional degree of freedom in acoustics communications applications; however, the long-distance propagation of these waves is largely unstudied. By employing BELLHOP’s ray tracing algorithm, the dominant features of a propagating OAM carrying vortex wave are tracked over long ranges (to and beyond 1 km) under various environmental conditions. This provides essential guidance in the design of the sending and receiving arrays of high-speed underwater communications systems, which rely on multiplexing acoustic OAMs.

Degradation and lifetime prediction of thermoplastic polyurethane encapsulants in seawater for underwater acoustic sensor applications

Degradation of thermoplastic polyurethane (TPU) encapsulants used in an underwater acoustic sensor (UAS) was examined by aging in seawater, and the lifetime of TPU encapsulants was estimated using an empirical approach based on accelerated lifetime test (ALT). The TPU encapsulants were placed in seawater and aged at temperatures of 85, 100 and 110 °C. TPU encapsulants underwent a hydrolysis reaction during aging in seawater, changing the chemical structure and molecular weight of TPU. Molecular dynamic (MD) simulation results described the hydrolysis degradation of TPU results in the decrease in tensile strength. Tensile tests were conducted on the aged TPU encapsulants, and then failure times for each of the accelerated conditions were estimated from the changes in tensile strength over aging time. The accelerated failure times were used for the lifetime prediction of TPU encapsulants at a use-stress level by using ReliaSoft's ALTA software. An empirical prediction model based on Arrhenius equation and Weibull distribution was determined for the lifetime prediction of the TPU encapsulant, facilitating lifetime estimations of TPU encapsulant at various failure criteria. A decrease in the tensile strength to 50% of the initial value was established as the failure criterion for lifetime prediction, indicating that half of TPU encapsulants will fail after aging about 20 years. Therefore, this result helps to determine the appropriate time for replacement of TPU encapsulant before failure.

Far-field analytical solutions of the non-homogeneous Helmholtz and wave equations for spatially non-localized sources.

Non-localized impulsive sources are ubiquitous in underwater acoustic applications. However, analytical expressions of their acoustic field are usually not available. In this work, far-field analytical solutions of the non-homogeneous scalar Helmholtz and wave equations are developed for a class of spatially extended impulsive sources. The derived expressions can serve as benchmarks to verify the accuracy of numerical solvers.

PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications

In underwater acoustic applications, time synchronization errors accumulate severely with time, owing to the unpredictable propagation delay change induced by mobility and low sound propagation speed. In previously reported schemes, error accumulation during underwater acoustic time synchronization occurred, decreasing their performance and affecting their applicability. To overcome this limitation, we propose packet-train-aided time synchronization (PTA-Sync) for underwater acoustic application. The proposed PTA-Sync adaptively tracks one-way travel time (OWTT) change using three-dimensional linear velocity vector, the transmit time difference and arrival time difference of two adjacent packets in a packet train, and the position information of a mobile reference node. Thus, this scheme enables us to reduce the synchronization error accumulation rate by estimating the propagation delay change more accurately. Simulation results show PTA-Sync achieves higher accuracy than existing OWTT-based synchronization schemes. Thus, PTA-Sync can be effectively used in underwater exploratory activities because it can successfully reduce time synchronization errors.

Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model

IntroductionIn underwater acoustic applications, the three-dimensional sound speed distribution has a significant impact on signal propagation. However, the traditional sound speed profile (SSP) measurement method requires a lot of manpower and time, and it is difficult to popularize. Satellite remote sensing can collect information on a large ocean surface area, from which the underwater information can be derived.MethodIn this paper, we propose a method for reconstructing the SSP based on an extensible end-to-end tree boosting (XGBoost) model. Combined with satellite remote sensing data and Argo profile data, it extracts the characteristic matrix of the SSP and analyzes the contribution rate of each order matrix to reduce the introduction of noise. The model inverts the SSP above 1000 m in the South China Sea by using the root mean square error (RMSE) as the precision evaluation index.ResultThe results showed that the XGBoost model could better reconstruct the SSP above 1000 m, with a RMSE of 1.75 m/s. Compared with the single empirical orthogonal function regression (sEOF-r) model of the linear regression method, the RMSE of the XGBoost model was reduced by 0.59 m/s.DiscussionFor this model, the RMSE of the inversion results was smaller, the robustness was better, and the regression performance was superior to that of the sEOF-r model at different depths. This study provided an efficient tree boosting model for SSP reconstruction, which could reliably and instantaneously monitor the 3D sound speed distribution.

Investigating cepstral methods for blind deconvolution

Cepstral methods are homomorphic signal processing techniques in which signals are transformed into the cepstral domain, typically to be averaged or filtered. In the cepstral domain, the convolution of the source signal and impulse response changes to a sum of the two parts by taking a logarithm in the frequency domain, followed by an inverse Fourier transform. Cepstral methods have been utilized in speech processing to separate vocal tract information from speech excitation, in marine mammal bioacoustics to classify vocalizations, and in seismic surveys to estimate source wavelets from airgun arrays, but do not appear to have been considered for blind deconvolution in other underwater acoustic applications. By assuming either the source signal or impulse response is stationary in time and/or space, averaging cepstral domain windows from a receiver array can suppress the non-stationary term. Here, we investigate the capabilities of cepstral averaging techniques for the purposes of blind deconvolution. We will exploit the spatial dependence of the impulse response across a receiver array to suppress this non-stationary term, while constructively combining the source signal term. We will consider the effects of different signal types, environmental characteristics, signal-to-noise ratios, and array design on its success. [Work supported by the ONR.]